The present invention relates to the segmentation of anatomical structures and more particularly to the use and editing of previously incomplete or incorrect segmentation of structures to achieve a correct segmentation.
Automatic segmentations of targets in images/volumes often require some degree of editing to meet the needs of a particular user (e.g., a physician). The question that the present invention will address is: Given a pre-existing segmentation (obtained through other means, e.g. an automatic algorithm), how does one edit the segmentation to correct problems that have occurred in a presegmentation step? A semi-automated segmentation algorithm like graph cuts is described in Y. Boykov and M.-P. Jolly, “Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images” in International Conference on Computer Vision, vol. I, July 2001, pp. 105-112, and the recently introduced random walker segmentation method was disclosed in U.S. patent application Ser. No. 11/029,442, filed Jan. 5, 2005, which is hereby incorporated by reference. It is also described in L. Grady and G. Funka-Lea, “Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials,” in Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. ECCV 2004 Workshops CVAMIA and MMBIA, ser. Lecture Notes in Computer Science, M. {hacek over (S)}onka, I. A. Kakadiaris, and J. Kybic, Eds., no. LNCS3117. Prague, Czech Republic: Springer, May 2004, pp. 230-245. These segmentation methods provide a high-quality intuitive interface for allowing a physician/user to segment images, but are not formulated to allow for importation or use of a prior segmentation. In the present invention, it will be shown how a prior segmentation may be seamlessly combined with graph cuts or the random walker algorithm to allow for editing, while maintaining the important property of both algorithms that an arbitrary segmentation may be achieved with enough interaction. The term presegmentation will be used to refer to the prior, pre-existing segmentation obtained through other means that will be presented for editing. Accordingly novel methods are required which can edit incorrect or incomplete presegmentations to achieve a correct segmentation of volumes and images.